{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dl/anaconda2/envs/py3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import ast\n",
    "import common\n",
    "from openpose.src import networks\n",
    "import cv2\n",
    "import numpy as np\n",
    "from estimator import TfPoseEstimator\n",
    "from networks import get_graph_path, model_wh\n",
    "from lifting.prob_model import Prob3dPose\n",
    "from lifting.draw import plot_pose\n",
    "from IPython.display import Image,display\n",
    "def show(img):\n",
    "    cv2.imwrite('/tmp/tmp.jpg', img)\n",
    "    display(Image(filename='/tmp/tmp.jpg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def human_score(human, num=18):\n",
    "    '''\n",
    "    计算一个human的整体score，如num-18时， 计算0~17的分数平均值，如果没有算0（因此会拉低分数）\n",
    "    '''\n",
    "    parts = human.body_parts\n",
    "    score = 0\n",
    "    for i in range(num):\n",
    "        if i in parts:\n",
    "            score += parts[i].score\n",
    "    return float(score) / num\n",
    "\n",
    "name2num = dict(\n",
    "    Nose =0,\n",
    "    Neck = 1,\n",
    "    RShoulder = 2,\n",
    "    RElbow = 3,\n",
    "    RWrist = 4,\n",
    "    LShoulder = 5,\n",
    "    LElbow = 6,\n",
    "    LWrist = 7,\n",
    "    RHip = 8,\n",
    "    RKnee = 9,\n",
    "    RAnkle = 10,\n",
    "    LHip = 11,\n",
    "    LKnee = 12,\n",
    "    LAnkle = 13,\n",
    "    REye = 14,\n",
    "    LEye = 15,\n",
    "    REar = 16,\n",
    "    LEar = 17,\n",
    "    Background = 18,\n",
    ")\n",
    "\n",
    "num2name = {name2num[key]: key for key in name2num}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OpenPoseEsitimator(object):\n",
    "    def __init__(self, scales=None, w=256, h=256, model_name='mobilenet_thin'):\n",
    "        self.scales = scales\n",
    "        self.w = w\n",
    "        self.h = h\n",
    "        self.model_name = 'mobilenet_thin'\n",
    "        self.e = TfPoseEstimator(get_graph_path(self.model_name), target_size=(self.w, self.h))\n",
    "        \n",
    "\n",
    "    def inference(self, image):\n",
    "        '''\n",
    "        只会推导出得分最大的那个，如果没有则返回None\n",
    "        image是opencv的格式\n",
    "        格式：名称->（宽上的坐标，高上的坐标）\n",
    "        '''\n",
    "        image_h = image.shape[0]\n",
    "        image_w = image.shape[1]\n",
    "        humans = self.e.inference(image, scales=self.scales)\n",
    "        if len(humans) == 0:\n",
    "            return None\n",
    "        scores = [human_score(human) for human in humans]\n",
    "\n",
    "        max_score = scores[0]\n",
    "        max_human = humans[0]\n",
    "        for i, score in enumerate(scores):\n",
    "            if score > max_score:\n",
    "                max_score = score\n",
    "                max_human = humans[i]  \n",
    "        ret = {num2name[num]: (int(max_human.body_parts[num].x * image_w + 0.5), int(max_human.body_parts[num].y * image_h + 0.5))\n",
    "               for num in max_human.body_parts\n",
    "              }\n",
    "        return ret\n",
    "\n",
    "    \n",
    "    def inference_and_draw(self, image):\n",
    "        '''\n",
    "        只会推导出得分最大的那个，如果没有则返回None\n",
    "        '''\n",
    "        humans = self.e.inference(image, scales=self.scales)\n",
    "        if len(humans) == 0:\n",
    "            return None\n",
    "        scores = [human_score(human) for human in humans]\n",
    "\n",
    "        max_score = scores[0]\n",
    "        max_human = humans[0]\n",
    "        for i, score in enumerate(scores):\n",
    "            if score > max_score:\n",
    "                max_score = score\n",
    "                max_human = humans[i]  \n",
    "        return TfPoseEstimator.draw_humans(image, [max_human], imgcopy=False)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 205, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('images/img_00000002.jpg', cv2.IMREAD_COLOR)\n",
    "e = OpenPoseEsitimator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "humans = e.inference_and_draw(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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qzwv8Cf21kstbm/4kfjAS+FdTm/55XUR8y1l/9GRV+uem+Kho+g/8JHoeh3OtxxfvIdOt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\nFAH/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(humans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
